/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#include "tensorflow/cc/ops/nn_ops_internal.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/grappler/devices.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/optimizers/remapper.h"
#include "tensorflow/core/grappler/utils/grappler_test.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/mkl_util.h"

namespace tensorflow {
namespace grappler {

class MklRemapperTest : public GrapplerTest {
 public:
  const string kAddNOp = "AddN";
  const string kAddOp = "Add";
  const string kAddV2Op = "AddV2";

 protected:
  void FuseConv2DWithBiasAndAddNOrAdd(const string& data_format,
                                      const string& activation, string add_op,
                                      bool add_with_bcast) {
    using ::tensorflow::ops::Placeholder;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape = (data_format == "NHWC")
                           ? ops::Placeholder::Shape({8, 32, 32, 3})
                           : ops::Placeholder::Shape({8, 3, 32, 32});
    auto input_shape_addn = ops::Placeholder::Shape({});
    if (data_format == "NHWC") {
      if (add_with_bcast)
        input_shape_addn = ops::Placeholder::Shape({128});
      else
        input_shape_addn = ops::Placeholder::Shape({8, 32, 32, 128});
    } else {
      if (add_with_bcast)
        input_shape_addn = ops::Placeholder::Shape({32});
      else
        input_shape_addn = ops::Placeholder::Shape({8, 128, 32, 32});
    }
    auto filter_shape = ops::Placeholder::Shape({1, 1, 3, 128});
    auto bias_shape = ops::Placeholder::Shape({128});

    auto input = Placeholder(s.WithOpName("input"), DT_FLOAT, input_shape);
    auto input_addn =
        Placeholder(s.WithOpName("input_addn"), DT_FLOAT, input_shape_addn);
    auto filter = Placeholder(s.WithOpName("filter"), DT_FLOAT, filter_shape);
    auto bias = Placeholder(s.WithOpName("bias"), DT_FLOAT, bias_shape);

    std::vector<int> strides = {1, 1, 1, 1};
    auto conv =
        ops::Conv2D(s.WithOpName("conv"), input, filter, strides, "SAME",
                    ops::Conv2D::Attrs().DataFormat(data_format));
    auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias,
                                 ops::BiasAdd::Attrs().DataFormat(data_format));

    auto addfetch = [&](::tensorflow::Input addop) {
      auto activate = s.WithOpName("activation");
      auto fetch = s.WithOpName("fetch");
      if (activation == "Relu") {
        ops::Identity(fetch, ops::Relu(activate, addop));
      } else if (activation == "Relu6") {
        ops::Identity(fetch, ops::Relu6(activate, addop));
      } else if (activation == "Elu") {
        ops::Identity(fetch, ops::Elu(activate, addop));
      } else if (activation == "LeakyRelu") {
        ops::Identity(fetch, ops::internal::LeakyRelu(activate, addop));
      } else {
        DCHECK(activation == "None");
        ops::Identity(fetch, addop);
      }
    };

    if (add_op == kAddNOp) {
      auto addn = ops::AddN(s.WithOpName(add_op),
                            std::initializer_list<Input>{input_addn, bias_add});
      addfetch(addn);
    } else if (add_op == kAddV2Op) {
      auto add = ops::AddV2(s.WithOpName(add_op), input_addn, bias_add);
      addfetch(add);
    } else {
      auto add = ops::Add(s.WithOpName(add_op), input_addn, bias_add);
      addfetch(add);
    }
    auto input_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(input_shape.shape_.dim_sizes()));
    auto input_addn_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(input_shape_addn.shape_.dim_sizes()));
    auto filter_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(filter_shape.shape_.dim_sizes()));
    auto bias_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(bias_shape.shape_.dim_sizes()));

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_tensor},
                 {"filter", filter_tensor},
                 {"bias", bias_tensor},
                 {"input_addn", input_addn_tensor}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    // Set Rewriter config to AGGRESSIVE so that we can use Placeholder shape
    // to test that Add with both inputs having same shape get fused with
    // Conv2D. Setting this config to AGGRESSIVE is not required for the feature
    // though.
    Remapper optimizer(RewriterConfig::AGGRESSIVE);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    bool check_fusion = !add_with_bcast;
    int found = 0;
    for (const NodeDef& node : output.node()) {
      auto fetch_node_name = activation != "None" ? "activation" : add_op;
      if (node.name() == fetch_node_name) {
        if (check_fusion) {
          EXPECT_EQ("_FusedConv2D", node.op());
          EXPECT_EQ("input", node.input(0));
          EXPECT_EQ("filter", node.input(1));

          EXPECT_EQ(2, node.attr().at("num_args").i());
          EXPECT_EQ("bias", node.input(2));
          EXPECT_EQ("input_addn", node.input(3));

          const auto fused_ops = node.attr().at("fused_ops").list().s();
          if (activation != "None") {
            EXPECT_EQ(3, fused_ops.size());
            EXPECT_EQ("BiasAdd", fused_ops[0]);
            EXPECT_EQ("Add", fused_ops[1]);
            EXPECT_EQ(activation, fused_ops[2]);
          } else {
            EXPECT_EQ(2, fused_ops.size());
            EXPECT_EQ("BiasAdd", fused_ops[0]);
            EXPECT_EQ("Add", fused_ops[1]);
          }
        } else {
          if (activation != "None") {
            EXPECT_EQ(node.op(), activation);
            ASSERT_EQ(node.input_size(), 1);
            EXPECT_EQ(node.input(0), add_op);
          } else {
            EXPECT_EQ(node.op(), add_op);
            ASSERT_EQ(node.input_size(), 2);
          }
        }
        found++;
      }
    }
    EXPECT_EQ(1, found);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    EXPECT_EQ(1, tensors_expected.size());
    EXPECT_EQ(1, tensors.size());
    // Using relative tolerance since oneDNN could produce different results
    // when float32 numbers need to be rounded during accumulation.
    test::ExpectClose(tensors_expected[0], tensors[0], 0, 1e-6);
  }
};

#define CREATE_CONV2DFUSION_TEST(data_format, addop, activation, bcast)                          \
  TEST_F(                                                                                        \
      MklRemapperTest,                                                                           \
      FuseConv2DWithBiasAnd##addop##_##data_format##_activation##activation##_addbcast##bcast) { \
    FuseConv2DWithBiasAndAddNOrAdd(#data_format, #activation, #addop, bcast);                    \
  }

#define CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(data_format, addop, bcast) \
  CREATE_CONV2DFUSION_TEST(data_format, addop, Relu, bcast);               \
  CREATE_CONV2DFUSION_TEST(data_format, addop, Relu6, bcast);              \
  CREATE_CONV2DFUSION_TEST(data_format, addop, Elu, bcast);                \
  CREATE_CONV2DFUSION_TEST(data_format, addop, LeakyRelu, bcast);          \
  CREATE_CONV2DFUSION_TEST(data_format, addop, None, bcast);

#define CREATE_CONV2DFUSION_ADD_NOBCAST_TEST(addop)            \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NHWC, addop, false); \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NCHW, addop, false);

CREATE_CONV2DFUSION_ADD_NOBCAST_TEST(AddN);

#define CREATE_CONV2DFUSION_ADD_BCAST_TEST(addop)              \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NHWC, addop, false); \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NCHW, addop, false); \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NHWC, addop, true);  \
  CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST(NCHW, addop, true);

CREATE_CONV2DFUSION_ADD_BCAST_TEST(Add);
CREATE_CONV2DFUSION_ADD_BCAST_TEST(AddV2);

#undef CREATE_CONV2DFUSION_ADD_NOBCAST_TEST
#undef CREATE_CONV2DFUSION_ADD_BCAST_TEST
#undef CREATE_CONV2DFUSION_ADD_ACTIVATION_TEST
#undef CREATE_CONV2DFUSION_TEST

#define REGISTER_TEST(NAME, T, INPUT)                                         \
  TEST_F(MklRemapperTest, NAME##_##T) {                                       \
    using ::tensorflow::ops::Placeholder;                                     \
                                                                              \
    for (const string& activation : {"Relu", "Relu6", "Elu", "None"}) {       \
      tensorflow::Scope s = tensorflow::Scope::NewRootScope();                \
                                                                              \
      auto input_shape = Placeholder::Shape({8, 32, 32, 3});                  \
      auto filter_shape = Placeholder::Shape({1, 1, 3, 1});                   \
      auto bias_shape = Placeholder::Shape({3});                              \
                                                                              \
      auto input = Placeholder(s.WithOpName("input"), DT_FLOAT, input_shape); \
      auto filter =                                                           \
          Placeholder(s.WithOpName("filter"), DT_FLOAT, filter_shape);        \
      auto bias = Placeholder(s.WithOpName("bias"), DT_FLOAT, bias_shape);    \
                                                                              \
      std::vector<int> strides = {1, 1, 1, 1};                                \
      auto conv = ops::DepthwiseConv2dNative(s.WithOpName("depthwise_conv"),  \
                                             input, filter, strides, "SAME"); \
      auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);     \
                                                                              \
      ops::Identity fetch = [&]() -> ops::Identity {                          \
        auto activate = s.WithOpName("activation");                           \
        auto fetch = s.WithOpName("fetch");                                   \
                                                                              \
        if (activation == "Relu") {                                           \
          return ops::Identity(fetch, ops::Relu(activate, bias_add));         \
        } else if (activation == "Relu6") {                                   \
          return ops::Identity(fetch, ops::Relu6(activate, bias_add));        \
        } else if (activation == "Elu") {                                     \
          return ops::Identity(fetch, ops::Elu(activate, bias_add));          \
        }                                                                     \
                                                                              \
        DCHECK(activation == "None");                                         \
        return ops::Identity(fetch, bias_add);                                \
      }();                                                                    \
                                                                              \
      auto input_t = GenerateRandomTensor<DT_FLOAT>({8, 32, 32, 3});          \
      auto filter_t = GenerateRandomTensor<DT_FLOAT>({1, 1, 3, 1});           \
      auto bias_t = GenerateRandomTensor<DT_FLOAT>({3});                      \
                                                                              \
      GrapplerItem item;                                                      \
      item.fetch = {"fetch"};                                                 \
      item.feed = {                                                           \
          {"input", input_t}, {"filter", filter_t}, {"bias", bias_t}};        \
      TF_CHECK_OK(s.ToGraphDef(&item.graph));                                 \
                                                                              \
      for (int i = 0; i < item.graph.node_size(); ++i) {                      \
        item.graph.mutable_node(i)->set_device("/device:CPU:0");              \
      }                                                                       \
                                                                              \
      Remapper optimizer(RewriterConfig::ON);                                 \
      GraphDef output;                                                        \
      TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));                \
                                                                              \
      int found = 0;                                                          \
      for (const NodeDef& node : output.node()) {                             \
        if (node.name() != "bias_add" && node.name() != "activation")         \
          continue;                                                           \
                                                                              \
        EXPECT_EQ(node.op(), "_FusedDepthwiseConv2dNative");                  \
        ASSERT_EQ(node.input_size(), 3);                                      \
        EXPECT_EQ(node.input(0), "input");                                    \
        EXPECT_EQ(node.input(1), "filter");                                   \
                                                                              \
        EXPECT_EQ(node.attr().at("num_args").i(), 1);                         \
        EXPECT_EQ(node.input(2), "bias");                                     \
                                                                              \
        const auto fused_ops = node.attr().at("fused_ops").list().s();        \
        if (node.name() == "bias_add") {                                      \
          ASSERT_EQ(fused_ops.size(), 1);                                     \
          EXPECT_EQ(fused_ops[0], "BiasAdd");                                 \
          found++;                                                            \
        }                                                                     \
        if (node.name() == "activation") {                                    \
          ASSERT_EQ(fused_ops.size(), 2);                                     \
          EXPECT_EQ(fused_ops[0], "BiasAdd");                                 \
          EXPECT_EQ(fused_ops[1], activation);                                \
          found++;                                                            \
        }                                                                     \
      }                                                                       \
      EXPECT_EQ(found, 1);                                                    \
                                                                              \
      auto tensors_expected =                                                 \
          EvaluateNodes(item.graph, item.fetch, item.feed);                   \
      ASSERT_EQ(tensors_expected.size(), 1);                                  \
      auto tensors = EvaluateNodes(output, item.fetch, item.feed);            \
      ASSERT_EQ(tensors.size(), 1);                                           \
      test::ExpectTensorNear<float>(tensors[0], tensors_expected[0], 1e-6);   \
    }                                                                         \
  }
REGISTER_TEST_ALL_TYPES(FuseDepthwiseConv2DWithBiasAndActivation);
#undef REGISTER_TEST

TEST_F(MklRemapperTest, FuseBatchNormWithRelu) {
  using ::tensorflow::ops::Placeholder;

  for (bool is_training : {true, false}) {
    for (bool has_side_input : {true, false}) {
      tensorflow::Scope s = tensorflow::Scope::NewRootScope();

      const int num_channels = 24;

      TensorShape channel_shape({num_channels});
      TensorShape empty_shape({0});

      auto input =
          Placeholder(s.WithOpName("input"), DT_FLOAT,
                      ops::Placeholder::Shape({2, 8, 8, num_channels}));
      auto input_cast = ops::Cast(s.WithOpName("input_cast"), input, DT_FLOAT);
      auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
      auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
      auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
      auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);

      float epsilon = 0.1f;
      auto fbn =
          ops::FusedBatchNormV3(s.WithOpName("fused_batch_norm"), input_cast,
                                scale, offset, mean, var,
                                ops::FusedBatchNormV3::IsTraining(is_training)
                                    .Epsilon(epsilon)
                                    .DataFormat("NHWC"));

      if (has_side_input) {
        auto side_input =
            Placeholder(s.WithOpName("side_input"), DT_FLOAT,
                        ops::Placeholder::Shape({2, 8, 8, num_channels}));
        auto side_input_cast =
            ops::Cast(s.WithOpName("side_input_cast"), side_input, DT_FLOAT);
        auto add = ops::Add(s.WithOpName("add"), fbn.y, side_input_cast);
        auto relu = ops::Relu(s.WithOpName("relu"), add);
      } else {
        auto relu = ops::Relu(s.WithOpName("relu"), fbn.y);
      }

      auto input_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
      auto scale_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
      auto offset_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
      auto mean_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
                                                               : channel_shape);
      auto var_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
                                                              : channel_shape);
      auto side_input_t =
          GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});

      GrapplerItem item;
      item.fetch = {"relu"};
      if (has_side_input)
        item.feed = {{"input", input_t},   {"scale", scale_t},
                     {"offset", offset_t}, {"mean", mean_t},
                     {"var", var_t},       {"side_input", side_input_t}};
      else
        item.feed = {{"input", input_t},
                     {"scale", scale_t},
                     {"offset", offset_t},
                     {"mean", mean_t},
                     {"var", var_t}};
      TF_ASSERT_OK(s.ToGraphDef(&item.graph));

      // Place all nodes on CPU.
      for (int i = 0; i < item.graph.node_size(); ++i) {
        item.graph.mutable_node(i)->set_device("/device:CPU:0");
      }

      Remapper optimizer(RewriterConfig::AGGRESSIVE);
      GraphDef output;
      TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));

      int found = 0;
      if (has_side_input) {
        for (const NodeDef& node : output.node()) {
          if (node.name() == "add") {
            EXPECT_EQ(node.op(), "Add");
            ASSERT_EQ(node.input_size(), 2);
            EXPECT_EQ(node.input(0), "fused_batch_norm");
            EXPECT_EQ(node.input(1), "side_input_cast");
            found++;
          }
          if (node.name() == "relu") {
            EXPECT_EQ(node.op(), "Relu");
            ASSERT_EQ(node.input_size(), 1);
            EXPECT_EQ(node.input(0), "add");
            found++;
          }
          if (node.name() == "fused_batch_norm") {
            EXPECT_EQ(node.op(), "FusedBatchNormV3");
            ASSERT_EQ(node.input_size(), 5);
            EXPECT_EQ(node.input(0), "input_cast");
            EXPECT_EQ(node.input(1), "scale");
            EXPECT_EQ(node.input(2), "offset");
            EXPECT_EQ(node.input(3), "mean");
            EXPECT_EQ(node.input(4), "var");
            found++;
          }
        }
        EXPECT_EQ(found, 3);
      } else {
        for (const NodeDef& node : output.node()) {
          if (node.name() == "relu") {
            EXPECT_EQ(node.op(), "Identity");
            ASSERT_EQ(node.input_size(), 1);
            EXPECT_EQ(node.input(0), "fused_batch_norm");
            found++;
          }
          if (node.name() == "fused_batch_norm") {
            EXPECT_EQ(node.op(), "_FusedBatchNormEx");
            ASSERT_EQ(node.input_size(), 5);
            EXPECT_EQ(node.input(0), "input_cast");
            EXPECT_EQ(node.input(1), "scale");
            EXPECT_EQ(node.input(2), "offset");
            EXPECT_EQ(node.input(3), "mean");
            EXPECT_EQ(node.input(4), "var");

            auto attr = node.attr();
            EXPECT_EQ(attr["num_side_inputs"].i(), 0);
            EXPECT_EQ(attr["activation_mode"].s(), "Relu");
            found++;
          }
        }
        EXPECT_EQ(found, 2);
      }

      auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
      ASSERT_EQ(tensors_expected.size(), 1);
      auto tensors = EvaluateNodes(output, item.fetch, item.feed);
      ASSERT_EQ(tensors.size(), 1);
      test::ExpectTensorNear<float>(tensors[0], tensors_expected[0], 1e-6);
    }
  }
}

TEST_F(MklRemapperTest, FuseMatMulWithBiasAddAndAdd) {
  using ::tensorflow::ops::Placeholder;

  for (const string& add_op : {"BiasAdd", "AddV2", "Add"}) {
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape = ops::Placeholder::Shape({4, 32});
    auto input_shape_add = ops::Placeholder::Shape({4, 8});
    auto filter_shape = ops::Placeholder::Shape({32, 8});
    auto bias_shape = ops::Placeholder::Shape({8});

    auto input = Placeholder(s.WithOpName("input"), DT_FLOAT, input_shape);
    auto input_add =
        Placeholder(s.WithOpName("input_add"), DT_FLOAT, input_shape_add);
    auto filter = Placeholder(s.WithOpName("filter"), DT_FLOAT, filter_shape);
    auto bias = Placeholder(s.WithOpName("bias"), DT_FLOAT, bias_shape);

    auto matmul = ops::MatMul(s.WithOpName("matmul"), input, filter);
    Output bias_add;
    if (add_op == "BiasAdd")
      bias_add = ops::BiasAdd(s.WithOpName("bias_add"), matmul, bias);
    else if (add_op == "AddV2")
      bias_add = ops::AddV2(s.WithOpName("bias_add"), matmul, bias);
    else if (add_op == "Add")
      bias_add = ops::Add(s.WithOpName("bias_add"), bias, matmul);

    auto fetch = s.WithOpName("fetch");
    auto add = ops::Add(s.WithOpName("add"), bias_add, input_add);

    ops::Identity(fetch, add);

    auto input_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(input_shape.shape_.dim_sizes()));
    auto input_add_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(input_shape_add.shape_.dim_sizes()));
    auto filter_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(filter_shape.shape_.dim_sizes()));
    auto bias_tensor = GenerateRandomTensor<DT_FLOAT>(
        TensorShape(bias_shape.shape_.dim_sizes()));

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_tensor},
                 {"filter", filter_tensor},
                 {"bias", bias_tensor},
                 {"input_add", input_add_tensor}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::AGGRESSIVE);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      auto fetch_node_name = "add";
      if (node.name() == fetch_node_name) {
        EXPECT_EQ("_FusedMatMul", node.op());
        EXPECT_EQ("input", node.input(0));
        EXPECT_EQ("filter", node.input(1));
        EXPECT_EQ(2, node.attr().at("num_args").i());
        EXPECT_EQ("bias", node.input(2));
        EXPECT_EQ("input_add", node.input(3));

        const auto fused_ops = node.attr().at("fused_ops").list().s();
        EXPECT_EQ(2, fused_ops.size());
        EXPECT_EQ("BiasAdd", fused_ops[0]);
        EXPECT_EQ("Add", fused_ops[1]);
        found++;
      }
    }
    EXPECT_EQ(1, found);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    EXPECT_EQ(1, tensors_expected.size());
    EXPECT_EQ(1, tensors.size());
    test::ExpectClose(tensors_expected[0], tensors[0], 0, 1e-6);
  }
}

class RelpaceAddWithBiasAddTest : public GrapplerTest {
 public:
  const string kAddOp = "Add";
  const string kAddV2Op = "AddV2";

 protected:
  template <DataType DTYPE>
  void RelpaceAddWithBiasAddDepthwiseConv2D(const string& add_op) {
    using ::tensorflow::ops::Placeholder;

    for (const string& activation : {"None", "Relu", "Relu6", "Elu"}) {
      tensorflow::Scope s = tensorflow::Scope::NewRootScope();

      auto input_shape = Placeholder::Shape({8, 32, 32, 3});
      auto filter_shape = Placeholder::Shape({1, 1, 3, 128});
      auto bias_shape = Placeholder::Shape({128 * 3});

      auto input = Placeholder(s.WithOpName("input"), DTYPE, input_shape);
      auto filter = Placeholder(s.WithOpName("filter"), DTYPE, filter_shape);
      auto bias = Placeholder(s.WithOpName("bias"), DTYPE, bias_shape);

      std::vector<int> strides = {1, 1, 1, 1};
      auto conv = ops::DepthwiseConv2dNative(s.WithOpName("depthwise_conv"),
                                             input, filter, strides, "SAME");

      Output bias_add;
      if (add_op == kAddV2Op) {
        bias_add = ops::AddV2(s.WithOpName(add_op), conv, bias);
      } else {
        bias_add = ops::Add(s.WithOpName(add_op), bias, conv);
      }

      ops::Identity fetch = [&]() -> ops::Identity {
        auto activate = s.WithOpName("activation");
        auto fetch = s.WithOpName("fetch");

        if (activation == "Relu") {
          return ops::Identity(fetch, ops::Relu(activate, bias_add));
        } else if (activation == "Relu6") {
          return ops::Identity(fetch, ops::Relu6(activate, bias_add));
        } else if (activation == "Elu") {
          return ops::Identity(fetch, ops::Elu(activate, bias_add));
        }

        return ops::Identity(fetch, bias_add);
      }();

      auto input_t = GenerateRandomTensor<DTYPE>({8, 32, 32, 3});
      auto filter_t = GenerateRandomTensor<DTYPE>({1, 1, 3, 128});
      auto bias_t = GenerateRandomTensor<DTYPE>({128 * 3});

      GrapplerItem item;
      item.fetch = {"fetch"};
      item.feed = {{"input", input_t}, {"filter", filter_t}, {"bias", bias_t}};
      TF_ASSERT_OK(s.ToGraphDef(&item.graph));

      // Place all nodes on CPU.
      for (int i = 0; i < item.graph.node_size(); ++i) {
        item.graph.mutable_node(i)->set_device("/device:CPU:0");
      }

      Remapper optimizer(RewriterConfig::AGGRESSIVE);
      GraphDef output;
      TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));

      int found = 0;
      for (const NodeDef& node : output.node()) {
        if (node.name() == "activation") {
          EXPECT_EQ(node.op(), "_FusedDepthwiseConv2dNative");
          ASSERT_GE(node.input_size(), 3);
          EXPECT_EQ(node.input(0), "input");
          EXPECT_EQ(node.input(1), "filter");
          EXPECT_EQ(node.attr().at("num_args").i(), 1);
          EXPECT_EQ(node.input(2), "bias");

          const auto fused_ops = node.attr().at("fused_ops").list().s();
          ASSERT_EQ(fused_ops.size(), 2);
          EXPECT_EQ(fused_ops[0], "BiasAdd");
          EXPECT_EQ(fused_ops[1], activation);

          found++;
        } else if (node.name() == add_op) {
          EXPECT_EQ(node.op(), "_FusedDepthwiseConv2dNative");
          ASSERT_GE(node.input_size(), 3);
          EXPECT_EQ(node.input(0), "input");
          EXPECT_EQ(node.input(1), "filter");
          EXPECT_EQ(node.attr().at("num_args").i(), 1);
          EXPECT_EQ(node.input(2), "bias");

          const auto fused_ops = node.attr().at("fused_ops").list().s();
          ASSERT_EQ(fused_ops.size(), 1);
          EXPECT_EQ(fused_ops[0], "BiasAdd");
          found++;
        }
      }
      EXPECT_EQ(found, 1);

      auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
      ASSERT_EQ(tensors_expected.size(), 1);
      auto tensors = EvaluateNodes(output, item.fetch, item.feed);
      ASSERT_EQ(tensors.size(), 1);

      if (DTYPE == DT_BFLOAT16)
        test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, 1e-2);
      else
        test::ExpectClose(tensors[0], tensors_expected[0], 1e-6);
    }
  }
};

#define CREATE_REPLACEADDWITHBIASADD_TEST_1(ops, addop, dtype)              \
  TEST_F(RelpaceAddWithBiasAddTest, RelpaceAddWithBiasAdd##ops##_##addop) { \
    RelpaceAddWithBiasAddDepthwiseConv2D<dtype>(#addop);                    \
  }
CREATE_REPLACEADDWITHBIASADD_TEST_1(DepthConv2D, AddV2, DT_FLOAT);
CREATE_REPLACEADDWITHBIASADD_TEST_1(DepthConv2D, Add, DT_FLOAT);

class FusedMatMulBiasAddAndGeluTest : public GrapplerTest {
 public:
  template <DataType DTYPE, bool is_pattern2>
  void RunTest() {
    using ::tensorflow::ops::Placeholder;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto lhs_shape = ops::Placeholder::Shape({8, 32});
    auto rhs_shape = ops::Placeholder::Shape({32, 64});
    auto bias_shape = ops::Placeholder::Shape({64});

    auto lhs = Placeholder(s.WithOpName("lhs"), DTYPE, lhs_shape);
    auto rhs = Placeholder(s.WithOpName("rhs"), DTYPE, rhs_shape);
    auto bias = Placeholder(s.WithOpName("bias"), DTYPE, bias_shape);

    auto matmul = ops::MatMul(s.WithOpName("matmul"), lhs, rhs);
    auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), matmul, bias);

    // Add Gelu approximate with smaller ops
    auto square_root_one_half_const =
        ops::Const(s.WithOpName("square_root_one_half_const"), {0.707106f}, {});
    // For some cases, eg. BF16, there will be a Cast, Const -> Cast -> Mul
    auto square_root_one_half = ops::Cast(s.WithOpName("square_root_one_half"),
                                          square_root_one_half_const, DTYPE);
    auto bias_add_times_square_root_one_half =
        ops::Mul(s.WithOpName("bias_add_times_square_root_one_half"), bias_add,
                 square_root_one_half);
    auto erf =
        ops::Erf(s.WithOpName("erf"), bias_add_times_square_root_one_half);

    auto one_const = ops::Const(s.WithOpName("one_const"), {1.0f}, {});
    // For some cases, eg. BF16, there will be a Cast, Const -> Cast -> AddV2
    auto one = ops::Cast(s.WithOpName("one"), one_const, DTYPE);
    auto erf_plus_one = ops::AddV2(s.WithOpName("one_plus_erf"), erf, one);

    auto one_half_const =
        ops::Const(s.WithOpName("one_half_const"), {0.5f}, {});
    // For some cases, eg. BF16, there will be a Cast, Const -> Cast -> Mul
    auto one_half = ops::Cast(s.WithOpName("one_half"), one_half_const, DTYPE);

    Output gelu;
    if (is_pattern2) {
      auto bias_add_times_one_half = ops::Mul(
          s.WithOpName("erf_plus_one_times_one_half"), bias_add, one_half);
      gelu = ops::Mul(s.WithOpName("fusion_output"), erf_plus_one,
                      bias_add_times_one_half);
    } else {
      auto erf_plus_one_times_one_half = ops::Mul(
          s.WithOpName("erf_plus_one_times_one_half"), erf_plus_one, one_half);
      auto gelu = ops::Mul(s.WithOpName("fusion_output"),
                           erf_plus_one_times_one_half, bias_add);
    }

    auto fetch = ops::Identity(s.WithOpName("fetch"), gelu);

    auto lhs_t = GenerateTensorWithSetRandom<DTYPE>({8, 32});
    auto rhs_t = GenerateTensorWithSetRandom<DTYPE>({32, 64});
    auto bias_t = GenerateTensorWithSetRandom<DTYPE>({64});

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"lhs", lhs_t}, {"rhs", rhs_t}, {"bias", bias_t}};
    TF_ASSERT_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef optimized_graph;
    TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &optimized_graph));
    int found = 0;
    for (const NodeDef& node : optimized_graph.node()) {
      if (node.name() == "fusion_output") {
        EXPECT_EQ(node.op(), "_FusedMatMul");
        ASSERT_GE(node.input_size(), 3);
        EXPECT_EQ(node.input(0), "lhs");
        EXPECT_EQ(node.input(1), "rhs");
        EXPECT_EQ(node.input(2), "bias");
        EXPECT_EQ(node.attr().at("num_args").i(), 1);
        const auto fused_ops = node.attr().at("fused_ops").list().s();
        ASSERT_EQ(fused_ops.size(), 2);
        EXPECT_EQ(fused_ops[0], "BiasAdd");
        EXPECT_EQ(fused_ops[1], "GeluExact");
        found++;
      }
    }
    EXPECT_EQ(1, found);

    // Evaluate result without remapper fusion
    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 1);

    auto tensors_evaluated =
        EvaluateNodes(optimized_graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_evaluated.size(), 1);
    test::ExpectClose(tensors_evaluated[0], tensors_expected[0], 1e-6);
  }
};

// Fused {MatMul + BiasAdd + Gelu-exact} has 2 subgraph patterns. We test both
// patterns with float32 and bfloat16 data types here. Gelu-approximate test is
// added in `tensorflow/python/grappler/remapper_test.py` since the pattern is
// changed by other optimizers before the remapper optimizer.
TEST_F(FusedMatMulBiasAddAndGeluTest, Float32GeluExact) {
  RunTest<DT_FLOAT, false>();
}
TEST_F(FusedMatMulBiasAddAndGeluTest, BFloat16GeluExact) {
  RunTest<DT_BFLOAT16, false>();
}
TEST_F(FusedMatMulBiasAddAndGeluTest, Float32GeluExact2) {
  RunTest<DT_FLOAT, true>();
}
TEST_F(FusedMatMulBiasAddAndGeluTest, BFloat16GeluExact2) {
  RunTest<DT_BFLOAT16, true>();
}

class MklFusedBatchMatMul : public MklRemapperTest {
 public:
  template <typename T>
  void VerifyFused(bool adjx, bool adjy) {
    using ::tensorflow::ops::Placeholder;
    using normal_generator = Eigen::internal::NormalRandomGenerator<T>;

    int b0 = 2;
    int b1 = 2;
    int m = 32;
    int k = 16;
    int n = 64;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape =
        adjx ? TensorShape({b0, b1, k, m}) : TensorShape({b0, b1, m, k});
    auto weight_shape =
        adjy ? TensorShape({b0, b1, n, k}) : TensorShape({b0, b1, k, n});
    auto add_shape = TensorShape({b0, 1, m, n});

    auto input_placeholder_shape = ops::Placeholder::Shape(input_shape);
    auto weight_placeholder_shape = ops::Placeholder::Shape(weight_shape);
    auto add_placeholder_shape = ops::Placeholder::Shape(add_shape);

    auto input = Placeholder(s.WithOpName("input"), DataTypeToEnum<T>::v(),
                             input_placeholder_shape);
    auto weight = Placeholder(s.WithOpName("weight"), DataTypeToEnum<T>::v(),
                              weight_placeholder_shape);
    auto addend = Placeholder(s.WithOpName("addend"), DataTypeToEnum<T>::v(),
                              add_placeholder_shape);

    auto batchmatmul =
        ops::BatchMatMulV2(s.WithOpName("batchmatmul"), input, weight,
                           ops::BatchMatMulV2::Attrs().AdjX(adjx).AdjY(adjy));
    auto scale_const = ops::Const(s.WithOpName("scale_const"), {0.1f});
    auto scale =
        ops::Cast(s.WithOpName("scale"), scale_const, DataTypeToEnum<T>::v());
    auto mul = ops::Multiply(s.WithOpName("mul"), batchmatmul, scale);
    auto add = ops::AddV2(s.WithOpName("add"), mul, addend);
    auto fetch = ops::Identity(s.WithOpName("fetch"), add);

    Tensor input_t = Tensor(DataTypeToEnum<T>::v(), input_shape);
    Tensor weight_t = Tensor(DataTypeToEnum<T>::v(), weight_shape);
    Tensor add_t = Tensor(DataTypeToEnum<T>::v(), add_shape);
    input_t.flat<T>() =
        input_t.flat<T>().template setRandom<normal_generator>();
    weight_t.flat<T>() =
        weight_t.flat<T>().template setRandom<normal_generator>();
    add_t.flat<T>() = add_t.flat<T>().template setRandom<normal_generator>();

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t}, {"weight", weight_t}, {"addend", add_t}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() == "add") {
        EXPECT_EQ("_MklFusedBatchMatMulV2", node.op());
        EXPECT_EQ("input", node.input(0));
        EXPECT_EQ("weight", node.input(1));
        EXPECT_EQ("scale", node.input(2));
        EXPECT_EQ("addend", node.input(3));
        const auto fused_ops = node.attr().at("fused_ops").list().s();
        EXPECT_EQ(2, fused_ops.size());
        EXPECT_EQ("Mul", fused_ops[0]);
        found++;
        EXPECT_EQ("Add", fused_ops[1]);
        found++;
      }
    }
    EXPECT_EQ(2, found);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    float atol = 1e-6, rtol = 1e-6;
    if (std::is_same<T, bfloat16>::value) {
      atol = 1e-2;
      rtol = 1e-2;
    }
    test::ExpectClose(tensors_expected[0], tensors[0], atol, rtol);
  }

  template <typename T>
  void VerifyPreceedingScalarMul(bool adjx, bool adjy) {
    using ::tensorflow::ops::Placeholder;
    using normal_generator = Eigen::internal::NormalRandomGenerator<T>;

    int b0 = 2;
    int b1 = 2;
    int m = 32;
    int k = 16;
    int n = 64;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape =
        adjx ? TensorShape({b0, b1, k, m}) : TensorShape({b0, b1, m, k});
    auto weight_shape =
        adjy ? TensorShape({b0, b1, n, k}) : TensorShape({b0, b1, k, n});
    auto add_shape = TensorShape({b0, 1, m, n});

    auto input_placeholder_shape = ops::Placeholder::Shape(input_shape);
    auto weight_placeholder_shape = ops::Placeholder::Shape(weight_shape);
    auto add_placeholder_shape = ops::Placeholder::Shape(add_shape);

    const DataType dtype = DataTypeToEnum<T>::v();
    auto input =
        Placeholder(s.WithOpName("input"), dtype, input_placeholder_shape);
    auto weight =
        Placeholder(s.WithOpName("weight"), dtype, weight_placeholder_shape);
    auto addend =
        Placeholder(s.WithOpName("addend"), dtype, add_placeholder_shape);

    auto scale_const = ops::Const(s.WithOpName("scale_const"), {0.1f});
    auto scale = ops::Cast(s.WithOpName("scale"), scale_const, dtype);
    auto mul = ops::Multiply(s.WithOpName("mul"), input, scale);
    auto batchmatmul =
        ops::BatchMatMulV2(s.WithOpName("batchmatmul"), mul, weight,
                           ops::BatchMatMulV2::Attrs().AdjX(adjx).AdjY(adjy));
    auto add = ops::Add(s.WithOpName("add"), batchmatmul, addend);
    auto fetch = ops::Identity(s.WithOpName("fetch"), add);

    Tensor input_t = Tensor(dtype, input_shape);
    Tensor weight_t = Tensor(dtype, weight_shape);
    Tensor add_t = Tensor(dtype, add_shape);
    input_t.flat<T>() =
        input_t.flat<T>().template setRandom<normal_generator>();
    weight_t.flat<T>() =
        weight_t.flat<T>().template setRandom<normal_generator>();
    add_t.flat<T>() = add_t.flat<T>().template setRandom<normal_generator>();

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t}, {"weight", weight_t}, {"addend", add_t}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() == "add") {
        EXPECT_EQ("_MklFusedBatchMatMulV2", node.op());
        EXPECT_EQ("input", node.input(0));
        EXPECT_EQ("weight", node.input(1));
        EXPECT_EQ("scale", node.input(2));
        EXPECT_EQ("addend", node.input(3));
        const auto fused_ops = node.attr().at("fused_ops").list().s();
        EXPECT_EQ(2, fused_ops.size());
        EXPECT_EQ("Mul", fused_ops[0]);
        found++;
        EXPECT_EQ("Add", fused_ops[1]);
        found++;
      }
    }
    EXPECT_EQ(2, found);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    float atol = 1e-6, rtol = 1e-6;
    if (std::is_same<T, bfloat16>::value) {
      atol = 1e-2;
      rtol = 1e-2;
    }
    test::ExpectClose(tensors_expected[0], tensors[0], atol, rtol);
  }
};

TEST_F(MklFusedBatchMatMul, MulAndAdd) {
  for (const auto adjx : {false, true})
    for (const auto adjy : {false, true}) {
      this->VerifyFused<float>(adjx, adjy);
      this->VerifyFused<bfloat16>(adjx, adjy);
    }
}

TEST_F(MklFusedBatchMatMul, MulAndAdd2) {
  for (const auto adjx : {false, true})
    for (const auto adjy : {false, true}) {
      this->VerifyPreceedingScalarMul<float>(adjx, adjy);
      this->VerifyPreceedingScalarMul<bfloat16>(adjx, adjy);
    }
}

class MklRemapperSwishTest : public GrapplerTest {
 protected:
  template <DataType DTYPE>
  void RunTest() {
    using ::tensorflow::ops::Placeholder;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    auto mul_shape = ops::Placeholder::Shape({64, 64});

    // We will test four sitations:
    //  1. y = x * sigmoid(x)
    //  2. y = sigmoid(x) * x
    //  3. y = sigmoid(x) * sigmoid(sigmoid(x))
    //  4. y = sigmoid(sigmoid(x)) * sigmoid(x)
    auto input = Placeholder(s.WithOpName("input"), DTYPE, mul_shape);
    auto sigmoid1 = ops::Sigmoid(s.WithOpName("sigmoid1"), input);
    auto sigmoid2 = ops::Sigmoid(s.WithOpName("sigmoid2"), input);
    auto sigmoid3_1 = ops::Sigmoid(s.WithOpName("sigmoid3_1"), input);
    auto sigmoid3_2 = ops::Sigmoid(s.WithOpName("sigmoid3_2"), sigmoid3_1);
    auto sigmoid4_1 = ops::Sigmoid(s.WithOpName("sigmoid4_1"), input);
    auto sigmoid4_2 = ops::Sigmoid(s.WithOpName("sigmoid4_2"), sigmoid4_1);
    auto mul1 = ops::Mul(s.WithOpName("mul1"), input, sigmoid1);
    auto mul2 = ops::Mul(s.WithOpName("mul2"), sigmoid2, input);
    auto mul3 = ops::Mul(s.WithOpName("mul3"), sigmoid3_1, sigmoid3_2);
    auto mul4 = ops::Mul(s.WithOpName("mul4"), sigmoid4_2, sigmoid4_1);
    auto fetch1 = ops::Identity(s.WithOpName("fetch1"), mul1);
    auto fetch2 = ops::Identity(s.WithOpName("fetch2"), mul2);
    auto fetch3 = ops::Identity(s.WithOpName("fetch3"), mul3);
    auto fetch4 = ops::Identity(s.WithOpName("fetch4"), mul4);
    auto mul_t = GenerateTensorWithSetRandom<DTYPE>({64, 64});

    GrapplerItem item;
    item.fetch = {"fetch1", "fetch2", "fetch3", "fetch4"};
    item.feed = {{"input", mul_t}};
    TF_ASSERT_OK(s.ToGraphDef(&item.graph));

    // Place all nodes on CPU.
    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() == "mul1") {
        EXPECT_EQ(node.op(), "_MklSwish");
        ASSERT_EQ(node.input_size(), 1);
        EXPECT_EQ(node.input(0), "input");
        ++found;
      }
      if (node.name() == "mul2") {
        EXPECT_EQ(node.op(), "_MklSwish");
        ASSERT_EQ(node.input_size(), 1);
        EXPECT_EQ(node.input(0), "input");
        ++found;
      }
      // mul3 won't be replaced by swish
      // Coz of the limitation of patternMatcher with commutative op
      if (node.name() == "mul4") {
        EXPECT_EQ(node.op(), "_MklSwish");
        ASSERT_EQ(node.input_size(), 1);
        EXPECT_EQ(node.input(0), "sigmoid4_1");
        ++found;
      }
    }
    EXPECT_EQ(found, 3);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 4);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    ASSERT_EQ(tensors.size(), 4);
    float atol = 1e-6, rtol = 1e-6;
    if (DTYPE == DT_BFLOAT16) {
      atol = 1e-2;
      rtol = 1e-2;
    }
    test::ExpectClose(tensors[0], tensors_expected[0], atol, rtol);
    test::ExpectClose(tensors[1], tensors_expected[1], atol, rtol);
    test::ExpectClose(tensors[2], tensors_expected[2], atol, rtol);
    test::ExpectClose(tensors[3], tensors_expected[3], atol, rtol);
  }
};

TEST_F(MklRemapperSwishTest, F32) { RunTest<DT_FLOAT>(); }
TEST_F(MklRemapperSwishTest, BF16) { RunTest<DT_BFLOAT16>(); }

class MklRemapperConv2dBiasAddSwishTest : public GrapplerTest {
 protected:
  template <DataType DTYPE>
  void RunTest() {
    using ::tensorflow::ops::Placeholder;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape = Placeholder::Shape({1, 32, 32, 3});
    auto filter_shape = Placeholder::Shape({16, 1, 1, 3});
    auto bias_shape = Placeholder::Shape({3});

    auto input = Placeholder(s.WithOpName("input"), DTYPE, input_shape);
    auto filter = Placeholder(s.WithOpName("filter"), DTYPE, filter_shape);
    auto bias = Placeholder(s.WithOpName("bias"), DTYPE, bias_shape);

    std::vector<int> strides = {1, 1, 1, 1};
    auto conv =
        ops::Conv2D(s.WithOpName("conv2d"), input, filter, strides, "SAME");
    auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);

    ops::Identity fetch = [&]() -> ops::Identity {
      auto activate = s.WithOpName("activation");
      auto fetch = s.WithOpName("fetch");
      auto sigmoid = ops::Sigmoid(s.WithOpName("sigmoid"), bias_add);
      return ops::Identity(fetch, ops::Mul(activate, bias_add, sigmoid));
    }();

    auto input_t = GenerateTensorWithSetRandom<DTYPE>({1, 32, 32, 3});
    auto filter_t = GenerateTensorWithSetRandom<DTYPE>({16, 1, 1, 3});
    auto bias_t = GenerateTensorWithSetRandom<DTYPE>({3});

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t}, {"filter", filter_t}, {"bias", bias_t}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() != "bias_add" && node.name() != "activation") continue;

      EXPECT_EQ(node.op(), "_FusedConv2D");
      ASSERT_EQ(node.input_size(), 3);
      EXPECT_EQ(node.input(0), "input");
      EXPECT_EQ(node.input(1), "filter");
      EXPECT_EQ(node.attr().at("num_args").i(), 1);
      EXPECT_EQ(node.input(2), "bias");
      const auto fused_ops = node.attr().at("fused_ops").list().s();
      EXPECT_EQ(node.name(), "activation");
      ASSERT_EQ(fused_ops.size(), 2);
      EXPECT_EQ(fused_ops[0], "BiasAdd");
      EXPECT_EQ(fused_ops[1], "_MklSwish");
      found++;
    }
    EXPECT_EQ(found, 1);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 1);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    ASSERT_EQ(tensors.size(), 1);
    float atol = 1e-6, rtol = 1e-4;
    if (DTYPE == DT_BFLOAT16) {
      atol = 1e-2;
      rtol = 1e-2;
    }
    test::ExpectClose(tensors[0], tensors_expected[0], atol, rtol);
  }
};

TEST_F(MklRemapperConv2dBiasAddSwishTest, F32) { RunTest<DT_FLOAT>(); }
TEST_F(MklRemapperConv2dBiasAddSwishTest, BF16) { RunTest<DT_BFLOAT16>(); }

class MklRemapperConv2dFusedBatchNormSwishTest : public GrapplerTest {
 protected:
  template <DataType DTYPE>
  void RunTest() {
    using ::tensorflow::ops::Placeholder;

    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    auto input_shape = Placeholder::Shape({1, 32, 32, 3});
    auto filter_shape = Placeholder::Shape({16, 1, 1, 3});
    auto bn_param_shape = Placeholder::Shape({3});

    auto input = Placeholder(s.WithOpName("input"), DTYPE, input_shape);
    auto filter = Placeholder(s.WithOpName("filter"), DTYPE, filter_shape);
    auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
    auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
    auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
    auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);
    float epsilon = 0.1f;

    std::vector<int> strides = {1, 1, 1, 1};
    auto conv =
        ops::Conv2D(s.WithOpName("conv2d"), input, filter, strides, "SAME");
    auto fbn = ops::FusedBatchNormV3(
        s.WithOpName("fused_batch_norm"), conv, scale, offset, mean, var,
        ops::FusedBatchNormV3::IsTraining(false).Epsilon(epsilon).DataFormat(
            "NHWC"));
    ops::Identity fetch = [&]() -> ops::Identity {
      auto activate = s.WithOpName("activation");
      auto fetch = s.WithOpName("fetch");
      auto sigmoid = ops::Sigmoid(s.WithOpName("sigmoid"), fbn.y);
      return ops::Identity(fetch, ops::Mul(activate, fbn.y, sigmoid));
    }();

    auto input_t = GenerateTensorWithSetRandom<DTYPE>({1, 32, 32, 3});
    auto filter_t = GenerateTensorWithSetRandom<DTYPE>({16, 1, 1, 3});
    auto scale_t = GenerateTensorWithSetRandom<DT_FLOAT>({3});
    auto offset_t = GenerateTensorWithSetRandom<DT_FLOAT>({3});
    auto mean_t = GenerateTensorWithSetRandom<DT_FLOAT>({3});
    auto var_t = GenerateTensorWithSetRandom<DT_FLOAT>({3});

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t},   {"filter", filter_t}, {"scale", scale_t},
                 {"offset", offset_t}, {"mean", mean_t},     {"var", var_t}};
    TF_CHECK_OK(s.ToGraphDef(&item.graph));

    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() != "activation") continue;

      EXPECT_EQ(node.op(), "_FusedConv2D");
      ASSERT_EQ(node.input_size(), 6);
      EXPECT_EQ(node.input(0), "input");
      EXPECT_EQ(node.input(1), "filter");
      EXPECT_EQ(node.attr().at("num_args").i(), 4);
      EXPECT_EQ(node.input(2), "scale");
      EXPECT_EQ(node.input(3), "offset");
      EXPECT_EQ(node.input(4), "mean");
      EXPECT_EQ(node.input(5), "var");
      const auto fused_ops = node.attr().at("fused_ops").list().s();
      EXPECT_EQ(node.name(), "activation");
      ASSERT_EQ(fused_ops.size(), 2);
      EXPECT_EQ(fused_ops[0], "FusedBatchNorm");
      EXPECT_EQ(fused_ops[1], "_MklSwish");
      found++;
    }
    EXPECT_EQ(found, 1);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 1);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    ASSERT_EQ(tensors.size(), 1);
    test::ExpectClose(tensors[0], tensors_expected[0], 1e-6, 1e-4);
  }
};

TEST_F(MklRemapperConv2dFusedBatchNormSwishTest, F32) { RunTest<DT_FLOAT>(); }

class MklFuseInstanceNormTest : public GrapplerTest {
 protected:
  template <DataType DTYPE>
  void FuseMklInstanceNorm5D_Runner(string FORMAT, string activation) {
    using ::tensorflow::ops::Placeholder;
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    TensorShape input_shape, filter_shape, add_shape, scale_shift_shape;
    if (FORMAT == "NCDHW") {
      input_shape = TensorShape({4, 8, 3, 3, 4});
      add_shape = TensorShape({1, 16, 1, 1, 1});
      scale_shift_shape = TensorShape({1, 16, 1, 1, 1});
    } else {
      input_shape = TensorShape({4, 3, 3, 4, 8});
      add_shape = TensorShape({1, 1, 1, 1, 16});
      scale_shift_shape = TensorShape({1, 1, 1, 1, 16});
    }

    filter_shape = TensorShape({1, 1, 1, 8, 16});

    auto input_t = GenerateTensorWithSetRandom<DTYPE>(input_shape);
    auto filter_t = GenerateTensorWithSetRandom<DTYPE>(filter_shape);
    auto add_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);
    auto scale_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);
    auto shift_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);

    typedef typename EnumToDataType<DTYPE>::Type T;
    T epsilon_value = static_cast<T>(0.00001);
    auto epsilon_tensor =
        GenerateConstantTensor<DTYPE>(TensorShape({}), epsilon_value);

    std::vector<int> strides = {1, 1, 1, 1, 1};
    auto input = Placeholder(s.WithOpName("input"), DTYPE,
                             ops::Placeholder::Shape(input_shape));
    auto filter = Placeholder(s.WithOpName("filter"), DTYPE,
                              ops::Placeholder::Shape(filter_shape));
    ops::Conv3D::Attrs conv_attrs = ops::Conv3D::Attrs().DataFormat(FORMAT);
    auto conv = ops::Conv3D(s.WithOpName("conv"), input, filter, strides,
                            "SAME", conv_attrs);

    auto add_const =
        ops::Const(s.WithOpName("add_const"), Input::Initializer(add_input));
    auto add = ops::Add(s.WithOpName("conv_add"), add_const, conv);

    auto r_indices =
        (FORMAT == "NCDHW")
            ? ops::Const(s.WithOpName("r_indices"), {2, 3, 4}, {3})
            : ops::Const(s.WithOpName("r_indices"), {1, 2, 3}, {3});
    ops::Mean::Attrs mean_attrs = ops::Mean::Attrs().KeepDims(true);
    auto mean = ops::Mean(s.WithOpName("mean"), add, r_indices, mean_attrs);
    auto s_diff = ops::SquaredDifference(s.WithOpName("s_diff"), add, mean);
    auto variance =
        ops::Mean(s.WithOpName("variance"), s_diff, r_indices, mean_attrs);

    auto epsilon =
        ops::Const(s.WithOpName("epsilon"), Input::Initializer(epsilon_tensor));
    auto add_1 = ops::AddV2(s.WithOpName("add_1"), variance, epsilon);
    auto rsqrt = ops::Rsqrt(s.WithOpName("rsqrt"), add_1);
    auto g_const =
        ops::Const(s.WithOpName("g_const"), Input::Initializer(scale_input));
    auto mul = ops::Mul(s.WithOpName("mul"), rsqrt, g_const);
    auto mul_1 = ops::Mul(s.WithOpName("mul_1"), add, mul);
    auto mul_2 = ops::Mul(s.WithOpName("mul_2"), mean, mul);
    auto b_const =
        ops::Const(s.WithOpName("b_const"), Input::Initializer(shift_input));
    auto sub = ops::Sub(s.WithOpName("sub"), b_const, mul_2);
    auto add_2 = ops::AddV2(s.WithOpName("add_2"), mul_1, sub);

    ops::Identity fetch = [&]() -> ops::Identity {
      auto activate = s.WithOpName("activation");
      auto fetch = s.WithOpName("fetch");
      if (activation == "None") {
        return ops::Identity(fetch, add_2);
      } else if (activation == "Relu") {
        return ops::Identity(fetch, ops::Relu(activate, add_2));
      } else if (activation == "LeakyRelu") {
        auto attr = ops::internal::LeakyRelu::Alpha(0.3f);
        return ops::Identity(fetch,
                             ops::internal::LeakyRelu(activate, add_2, attr));
      }
      return ops::Identity(fetch, add_2);
    }();

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t}, {"filter", filter_t}};

    TF_ASSERT_OK(s.ToGraphDef(&item.graph));

    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));

    string fused_node_name = (activation == "None") ? "add_2" : "activation";
    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() == fused_node_name) {
        EXPECT_EQ(node.op(), "_MklFusedInstanceNorm");
        ASSERT_EQ(node.input_size(), 3);
        EXPECT_EQ(node.input(0), "conv_add");
        EXPECT_EQ(node.input(1), "g_const");
        EXPECT_EQ(node.input(2), "b_const");
        found++;
      }
    }

    EXPECT_EQ(found, 1);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 1);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    ASSERT_EQ(tensors.size(), 1);
    if (DTYPE == DT_BFLOAT16) {
      test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, 1e-2);
    } else {
      test::ExpectClose(tensors[0], tensors_expected[0], 2e-6, 1e-6);
    }
  }

  template <DataType DTYPE>
  void FuseMklInstanceNorm4D_Runner(string FORMAT, string activation) {
    using ::tensorflow::ops::Placeholder;
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();

    TensorShape input_shape, filter_shape, add_shape, scale_shift_shape;
    if (FORMAT == "NCHW") {
      input_shape = TensorShape({4, 3, 32, 32});
      add_shape = TensorShape({1, 3, 1, 1});
      scale_shift_shape = TensorShape({1, 3, 1, 1});
    } else {
      input_shape = TensorShape({4, 32, 32, 3});
      add_shape = TensorShape({1, 1, 1, 3});
      scale_shift_shape = TensorShape({1, 1, 1, 3});
    }
    filter_shape = TensorShape({2, 2, 3, 3});

    auto input_t = GenerateTensorWithSetRandom<DTYPE>(input_shape);
    auto filter_t = GenerateTensorWithSetRandom<DTYPE>(filter_shape);
    auto add_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);
    auto scale_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);
    auto shift_input = GenerateTensorWithSetRandom<DTYPE>(scale_shift_shape);

    typedef typename EnumToDataType<DTYPE>::Type T;
    T epsilon_value = static_cast<T>(0.00001);
    auto epsilon_tensor =
        GenerateConstantTensor<DTYPE>(TensorShape({}), epsilon_value);

    std::vector<int> strides = {1, 1, 1, 1};
    auto input = Placeholder(s.WithOpName("input"), DTYPE,
                             ops::Placeholder::Shape(input_shape));
    auto filter = Placeholder(s.WithOpName("filter"), DTYPE,
                              ops::Placeholder::Shape(filter_shape));
    ops::Conv2D::Attrs conv_attrs = ops::Conv2D::Attrs().DataFormat(FORMAT);
    auto conv = ops::Conv2D(s.WithOpName("conv"), input, filter, strides,
                            "SAME", conv_attrs);

    auto add_const =
        ops::Const(s.WithOpName("add_const"), Input::Initializer(add_input));
    auto add = ops::Add(s.WithOpName("conv_add"), add_const, conv);

    auto r_indices = (FORMAT == "NCHW")
                         ? ops::Const(s.WithOpName("r_indices"), {2, 3}, {2})
                         : ops::Const(s.WithOpName("r_indices"), {1, 2}, {2});
    ops::Mean::Attrs mean_attrs = ops::Mean::Attrs().KeepDims(true);
    auto mean = ops::Mean(s.WithOpName("mean"), add, r_indices, mean_attrs);
    auto s_diff = ops::SquaredDifference(s.WithOpName("s_diff"), add, mean);
    auto variance =
        ops::Mean(s.WithOpName("variance"), s_diff, r_indices, mean_attrs);

    auto epsilon =
        ops::Const(s.WithOpName("epsilon"), Input::Initializer(epsilon_tensor));
    auto add_1 = ops::AddV2(s.WithOpName("add_1"), variance, epsilon);
    auto rsqrt = ops::Rsqrt(s.WithOpName("rsqrt"), add_1);
    auto g_const =
        ops::Const(s.WithOpName("g_const"), Input::Initializer(scale_input));
    auto mul = ops::Mul(s.WithOpName("mul"), rsqrt, g_const);
    auto mul_1 = ops::Mul(s.WithOpName("mul_1"), add, mul);
    auto mul_2 = ops::Mul(s.WithOpName("mul_2"), mean, mul);
    auto b_const =
        ops::Const(s.WithOpName("b_const"), Input::Initializer(shift_input));
    auto sub = ops::Sub(s.WithOpName("sub"), b_const, mul_2);
    auto add_2 = ops::AddV2(s.WithOpName("add_2"), mul_1, sub);

    ops::Identity fetch = [&]() -> ops::Identity {
      auto activate = s.WithOpName("activation");
      auto fetch = s.WithOpName("fetch");
      if (activation == "None") {
        return ops::Identity(fetch, add_2);
      } else if (activation == "Relu") {
        return ops::Identity(fetch, ops::Relu(activate, add_2));
      } else if (activation == "LeakyRelu") {
        auto attr = ops::internal::LeakyRelu::Alpha(0.3f);
        return ops::Identity(fetch,
                             ops::internal::LeakyRelu(activate, add_2, attr));
      }
      return ops::Identity(fetch, add_2);
    }();

    GrapplerItem item;
    item.fetch = {"fetch"};
    item.feed = {{"input", input_t}, {"filter", filter_t}};

    TF_ASSERT_OK(s.ToGraphDef(&item.graph));

    for (int i = 0; i < item.graph.node_size(); ++i) {
      item.graph.mutable_node(i)->set_device("/device:CPU:0");
    }

    Remapper optimizer(RewriterConfig::ON);
    GraphDef output;
    TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));

    string fused_node_name = (activation == "None") ? "add_2" : "activation";
    int found = 0;
    for (const NodeDef& node : output.node()) {
      if (node.name() == fused_node_name) {
        EXPECT_EQ(node.op(), "_MklFusedInstanceNorm");
        ASSERT_EQ(node.input_size(), 3);
        EXPECT_EQ(node.input(0), "conv_add");
        EXPECT_EQ(node.input(1), "g_const");
        EXPECT_EQ(node.input(2), "b_const");
        found++;
      }
    }

    EXPECT_EQ(found, 1);

    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
    ASSERT_EQ(tensors_expected.size(), 1);
    auto tensors = EvaluateNodes(output, item.fetch, item.feed);
    ASSERT_EQ(tensors.size(), 1);
    if (DTYPE == DT_BFLOAT16) {
      test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, 1e-2);
    } else {
      test::ExpectClose(tensors[0], tensors_expected[0], 2e-6, 1e-6);
    }
  }

  template <DataType DTYPE>
  void FuseMklInstanceNorm4D(string FORMAT, bool add_activation = false) {
    if (!add_activation) {
      return FuseMklInstanceNorm4D_Runner<DTYPE>(FORMAT, "None");
    }
    for (const string& activation : {"Relu", "LeakyRelu"}) {
      FuseMklInstanceNorm4D_Runner<DTYPE>(FORMAT, activation);
    }
  }

  template <DataType DTYPE>
  void FuseMklInstanceNorm5D(string FORMAT, bool add_activation = false) {
    if (!add_activation) {
      return FuseMklInstanceNorm5D_Runner<DTYPE>(FORMAT, "None");
    }
    for (const string& activation : {"Relu", "LeakyRelu"}) {
      FuseMklInstanceNorm5D_Runner<DTYPE>(FORMAT, activation);
    }
  }
};

TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNorm5D_FP32_NDHWC) {
  FuseMklInstanceNorm5D<DT_FLOAT>("NDHWC");
}
TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNorm5D_FP32_NCDHW) {
  FuseMklInstanceNorm5D<DT_FLOAT>("NCDHW");
}
TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNorm4D_FP32_NHWC) {
  FuseMklInstanceNorm4D<DT_FLOAT>("NHWC");
}
TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNorm4D_FP32_NCHW) {
  FuseMklInstanceNorm4D<DT_FLOAT>("NCHW");
}
TEST_F(MklFuseInstanceNormTest,
       FuseMklInstanceNormWithActivation5D_FP32_NDHWC) {
  FuseMklInstanceNorm5D<DT_FLOAT>("NDHWC", true);
}
TEST_F(MklFuseInstanceNormTest,
       FuseMklInstanceNormWithActivation5D_FP32_NCDHW) {
  FuseMklInstanceNorm5D<DT_FLOAT>("NCDHW", true);
}
TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNormWithActivation4D_FP32_NHWC) {
  FuseMklInstanceNorm4D<DT_FLOAT>("NHWC", true);
}
TEST_F(MklFuseInstanceNormTest, FuseMklInstanceNormWithActivation4D_FP32_NCHW) {
  FuseMklInstanceNorm4D<DT_FLOAT>("NCHW", true);
}

}  // namespace grappler
}  // namespace tensorflow
#endif  // INTEL_MKL && ENABLE_MKL
